Subtypes Of Lung Adenocarcinoma Research Articles

Explainable 18F-FDG PET/CT radiomics model for predicting EGFR mutation status in lung adenocarcinoma: a two-center study

PurposeTo establish an explainable 18F-FDG PET/CT-derived prediction model to identify EGFR mutation status and subtypes (EGFR wild, EGFR-E19, and EGFR-E21) in lung adenocarcinoma (LUAD).MethodsBaseline 18F-FDG PET/CT images of 478 patients with LUAD from 2 hospitals were collected. Data from hospital A (n = 390) was randomly split into a training group (n = 312) and an internal test group (n = 78), with data from hospital B (n = 88) utilized for external test. Further, a total of 4,760 handcrafted radiomics features (HRFs) were extracted from PET/CT scans. Candidates for the prediction model were constructed by cross-combinations of 11 feature selection methods and 7 classifiers. The optimal model was determined by combining the results of cross-center data validation and model visualization (Yellowbrick). The predictive performance was assessed via receiver operating characteristic curve, confusion matrix and classification report. Four explainable artificial intelligence technologies were used for optimal model interpretation.ResultsSex and SUVmax were selected as clinical risk factors, which were then combined with 8 robust PET/CT HRFs to establish the models. The optimal performance was obtained by combining a light gradient boosting machine classifier with random forest feature selection method achieving an optimal performance with a macro-average AUC of 0.75 in the internal test group and 0.81 in the external test group.ConclusionThe explainable EGFR mutation status prediction model have certain clinical practicability and good generalization performance, which may help in the timely selection of treatment options and prognosis prediction in patients with LUAD.

C6 and KLRG2 are pyroptosis subtype-related prognostic biomarkers and correlated with tumor-infiltrating lymphocytes in lung adenocarcinoma

Pyroptosis plays an important role in lung adenocarcinoma (LUAD). In this study, we aimed to explore the pyroptosis-related gene (PRG) expression pattern and to identify promising pyroptosis-related biomarkers to improve the prognosis of LUAD. The gene expression profiles and clinical information of LUAD patients were downloaded from the Cancer Genome Atlas (TCGA), and validation cohort information was extracted from the Gene Expression Omnibus database. Gene expression data were analyzed using the limma package and visualized using the ggplot2 package as well as the pheatmap package in R software. Functional enrichment analysis was also performed for the 44 differentially expressed PRGs (DEPRGs). Then, consensus clustering revealed pyroptosis-related tumor subtypes, and differentially expressed genes (DEGs) were screened according to the subtypes. Next, univariate Cox and multivariate Cox regression analyses were used to identify independent prognostic PRGs. After overlapping DEGs and the Lasso regression analysis-based prognostic genes, the predictive risk model was established and validated. Correlation analysis between PRGs and clinicopathological variables was also explored. Finally, the TIMER and TISIDB databases were used to further explore the correlation analysis between immune cell infiltration levels, the risk score, and clinicopathological variables in the predictive risk model. A total of 52 genes from the PubMed were identified as PRGs, and 44 of the 52 genes were pooled as DEPRGs. The most significant GO term was “collagen trimer” (P = 2.46E-13), and KEGG analysis results indicated that 44 DEPRGs were significantly enriched in Salmonella infection (P < 0.001). Then, consensus clustering analysis divided LUAD patients into two clusters, and a total of 79 DEGs were identified according to these cluster subtypes. Subsequently, univariate and multivariate Cox regression analyses were used to identify 12 genes that could serve as independent prognostic indicators and we also performed Lasso regression analysis and screened 23 DEGs. After overlapping 23 DEGs and 12 genes, only 4 (KLRG2, MAPK4, C6 and SFRP5) of 12 genes were selected for the further exploration of the prognostic pattern. Survival analysis results indicated that this risk model effectively predicted the prognosis (P < 0.001). Combined with the correlation analysis results between the 4 genes and clinicopathological variables, C6 and KLRG2 were screened as prognostic genes. In this study, we constructed a predictive risk model and identified two pyroptosis subtype-related gene expression patterns to improve the prognosis of LUAD. Understanding the subtypes of LUAD is helpful for accurately characterizing the LUAD and developing personalized treatment.

Classification of histological subtypes of non-small cell lung cancer using computerized tomography texture analysis

Objective: This study aimed to differentiate between the two main histological subtypes of non-small cell lung cancer using a non-invasive technique, computerized tomography texture analysis. Method: We included 53 patients. All patients were histopathologically proven non-small cell lung cancer cases. All patients underwent thorax CT scans. In CT images, the differences present in the texture features of adenocarcinoma and squamous cell carcinoma, which are the two main histological subtypes of non-small cell lung cancer, were determined by the consensus of two radiologists for computerized tomography-based texture analysis. Results: A total of 44 texture features were extracted, including 12 first-order features and 32 second-order features derived from gray-level co-occurrence matrix (GLCM), gray-level run-length matrix (GLRLM), neighborhood gray-level different matrix (NGLDM), and gray-level zone length matrix (GLZLM) features in 51 CT images. None of the evaluated texture parameters were statistically significant. However, in patients with squamous cell lung cancer, the values of Intensity Histogram, NGTDM Complexity, and Intensity Based Robust Mean Absolute Deviation higher from adenocarcinoma patients and had the highest area under the curve in roc analyses (0.727, 0.664, 0.666 respectively) Conclusion: Intensity Histogram, NGTDM Complexity, and Intensity Based Robust Mean Absolute Deviation features can be used to differentiate between the subtypes of non-small cell lung cancer, adenocarcinoma and squamous cell carcinoma. These features were highly associated with the high intratumoral heterogeneity of squamous cell lung cancer.

Deep learning-based characterization of pathological subtypes in lung invasive adenocarcinoma utilizing 18F-deoxyglucose positron emission tomography imaging

SummaryObjectiveTo evaluate the diagnostic efficacy of a deep learning (DL) model based on PET/CT images for distinguishing and predicting various pathological subtypes of invasive lung adenocarcinoma.MethodsA total of 250 patients diagnosed with invasive lung cancer were included in this retrospective study. The pathological subtypes of the cancer were recorded. PET/CT images were analyzed, including measurements and recordings of the short and long diameters on the maximum cross-sectional plane of the CT image, the density of the lesion, and the associated imaging signs. The SUVmax, SUVmean, and the lesion's long and short diameters on the PET image were also measured. A manual diagnostic model was constructed to analyze its diagnostic performance across different pathological subtypes. The acquired images were first denoised, followed by data augmentation to expand the dataset. The U-Net network architecture was then employed for feature extraction and network segmentation. The classification network was based on the ResNet residual network to address the issue of gradient vanishing in deep networks. Batch normalization was applied to ensure the feature matrix followed a distribution with a mean of 0 and a variance of 1. The images were divided into training, validation, and test sets in a ratio of 6:2:2 to train the model. The deep learning model was then constructed to analyze its diagnostic performance across different pathological subtypes.ResultsStatistically significant differences (P < 0.05) were observed among the four different subtypes in PET/CT imaging performance. The AUC and diagnostic accuracy of the manual diagnostic model for different pathological subtypes were as follows: APA: 0.647, 0.664; SPA: 0.737, 0.772; PPA: 0.698, 0.780; LPA: 0.849, 0.904. Chi-square tests indicated significant statistical differences among these subtypes (P < 0.05). The AUC and diagnostic accuracy of the deep learning model for the different pathological subtypes were as follows: APA: 0.854, 0.864; SPA: 0.930, 0.936; PPA: 0.878, 0.888; LPA: 0.900, 0.920. Chi-square tests also indicated significant statistical differences among these subtypes (P < 0.05). The Delong test showed that the diagnostic performance of the deep learning model was superior to that of the manual diagnostic model (P < 0.05).ConclusionsThe deep learning model based on PET/CT images exhibits high diagnostic efficacy in distinguishing and diagnosing various pathological subtypes of invasive lung adenocarcinoma, demonstrating the significant potential of deep learning techniques in accurately identifying and predicting disease subgroups.

Multi-site DNA methylation alterations of peripheral blood mononuclear cells serve as novel biomarkers for the diagnosis of AIS/stage I lung adenocarcinoma: A multi-center cohort study.

Early diagnosis remains an obstacle for improving the outcome of lung adenocarcinoma (LUAD). DNA methylation changes in peripheral blood mononuclear cells (PBMCs) could reflect immune response to tumorigenesis, providing the theoretical basis for early cancer diagnosis based on immune cell profiling. This multi-center study evaluated the DNA methylation patterns based on PBMCs samples from 1115 individuals at nine medical centers. Genome-wide DNA methylation profiling of PBMCs in a discovery cohort (35 LUAD patients and 50 healthy controls) was performed using Illumina 850K microarray. Candidate differentially methylated CpG positions (DMPs) were selected and validated in a two-step DMPs screening cohort (65 LUAD patients and 80 healthy controls) by pyrosequencing and multiple target region methylation enrichment sequencing (MTRMES). Then, an early LUAD Diagnostic Panel (LDP score) based on multi-site methylation-specific chip-based digital PCR was constructed in a training set and then confirmed in a validation set from the LDP score development cohort (389 AIS/stage I LUAD patients and 293 healthy controls). Besides, we included 157 other cancer patients, including 52 gastric cancer (GC) patients, 50 breast cancer (BC) patients, and 55 colorectal cancer (CRC) patients to assess the specificity of LDP score. In addition, we also evaluated the early warning ability of LDP score for LUAD in a prospective cohort (46 people who were at high-risk of developing LC). A total of 1415 LUAD-specific DMPs were identified. Then, six DMPs were selected for validation and three DMPs were finally verified. The LDP score was constructed by combining the three DMPs, age, and gender, and showed an AUC of 0.916, sensitivity of 88.17%, and specificity of 80.20% in combined set, outperforming traditional methods, such as CEA and CT (detection rate: 87.79% vs. 4.69%; 87.79% vs. 35.21%). This diagnostic performance was confirmed in sub-types of LUAD with clinical challenges, such as 6-20mm LUAD (AUC: 0.914, 95%CI: 0.889-0.934) and ground-glass nodules (AUC: 0.916, 95%CI: 0.889-0.938). Importantly, our LDP score had significant improvement in terms of selecting high-risk individuals who should receive low-dose computed tomography (87.80% vs. 9.28%). Remarkably, LDP score could predict LUAD around two years before clinical diagnosis in our prospective cohort. The novel developed LDP score represented a convenient and effective assay for the detection of AIS/stage I LUAD with high sensitivity and specificity, and had demonstrated unique advantages over traditional detection methods.

Microbiota modulate immune repertories in lung adenocarcinoma via microbiota-immunity interactive network.

While the resident microbiome of tumors has been shown to be associated with the occurrence and progression of non-small cell lung cancer, there remains a significant knowledge gap in understanding the correlation between the microbial spectrum and immunity response to cancer therapy. In the case of lung adenocarcinoma (LUAD), the tumor microenvironment, encompassing a diverse array of microbes and immune cells, plays a crucial role in modulating therapeutic response. Towards comprehending the underlying mechanism, we present the microbe-immunity interactive networks to delineate the microbiota and immunity repertoires for two distinct molecular subtypes in LUAD. We obtained multi-omics data of LUAD patients from the publicly available database. In this study, we conducted a systematic exploration of the microbial and immunological etiology of cancer prognosis, by integrating the microbiome, genome, transcriptome, and clinic data. The mutational signature analysis, transcriptome analysis, gene set enrichment analysis, and microbiota-immunity network analysis were performed. Based on the transcriptome repertories, we classified the patients into two molecular subtypes and observed that the overall survival of molecular subtype 2 (MS2) was notably shortened. We identified the microbial biomarkers in patients that distinguished between these molecular subtypes. The significant up-regulation of γδT and neutrophil in MS2, suggesting the inflammation augmentation and stimulation of γδT activation. What is more, the MS2 are characterized by a correlation network between microbiota biomarkers and γδT cell, which may contribute to suppression of anti-tumor immunity and poor overall survival. Our findings not only display the repertoires of tumor microbiota and immune cells, but also elucidate the potential contribution of the microbiota-immunity correlation network to unfavorable overall survival and therapeutic resistance, thereby exerting profound implications on future LUAD therapy.

Preoperative markers for identifying CT ≤2 cm solid nodules of lung adenocarcinoma based on image deep learning.

The solid pattern is a highly malignant subtype of lung adenocarcinoma. In the current era of transitioning from lobectomy to sublobar resection for the surgical treatment of small lung cancers, preoperative identification of this subtype is highly important for patient surgical approach selection and long-term prognosis. A total of 1489 patients with clinical stage IA1-2 primary lung adenocarcinoma were enrolled. Based on patient clinical characteristics and lung imaging features obtained via deep learning, highly correlated diagnostic factors were identified through LASSO regression and decision tree analysis. Subsequently, a logistic model and nomogram were constructed. A restricted cubic spline (RCS) was used to calculate the optimal inflection point of quantitative data and the differences between the groups. The three-dimensional proportion of solid component (PSC), sex, and smoking status was identified as being highly correlated diagnostic factors for solid predominant adenocarcinoma. The logistic model had good prediction efficiency, and the area under the ROC curve was 0.85. Decision curve analysis demonstrated that the application of diagnostic factors can improve patient outcomes. RCS analysis indicated that the proportion of solid adenocarcinomas increased by 4.6 times when the PSC was ≥72%. A PSC of 72% is a good cutoff point. The preoperative diagnosis of solid-pattern adenocarcinoma can be confirmed by typical imaging features and clinical characteristics, assisting the thoracic surgeon in developing a more precise surgical plan.

The role of local ablative therapy in patients with advanced invasive mucinous adenocarcinoma of the lung

PurposeInvasive mucinous adenocarcinoma (IMA) of the lungs is a rare subtype of lung adenocarcinoma with a limited understanding of its prognosis, particularly in advanced stages. This study aimed to assess the prognosis of patients with advanced IMA by focusing on treatment modalities.MethodsThis single-center retrospective study evaluated 33 patients with IMAs diagnosed with advanced-stage disease or disease progression after curative treatment between 2011 and 2021. The primary outcome was overall survival (OS), and the secondary outcome was progression-free survival (PFS). OS and PFS were calculated from the date of the diagnosis of advanced IMA.ResultsThe study cohort included 13 patients at the initial advanced stage and 20 patients who progressed after curative treatment. Treatment modalities included conventional chemotherapy in 24 patients (72.7%), targeted therapy in seven (21.2%), immunotherapy in 13 (39.4%), and local ablative therapy (LAT) in 13 (39.4%). The median OS was 32 months (95% confidence interval [CI], 2.9–61.0), with LAT significantly associated with improved OS compared to non-LAT treatment (not reached vs. 11.3 months, p = 0.001). However, there was no significant difference in OS based on conventional chemotherapy (p = 0.396), targeted therapy (p = 0.655), or immunotherapy (p = 0.992). In multivariate analysis, LAT remained an independent prognostic factor for OS (hazard ratio, 0.125; 95% CI, 0.026–0.608; p = 0.01). PFS was 8.6 months (95% CI, 3.6–13.7), with no significant differences observed among the treatment modalities.ConclusionOur findings suggest that LAT may provide favorable survival outcomes in patients with advanced IMA.

Impact of lung adenocarcinoma subtypes on survival and timing of brain metastases.

Lung cancer is a devastating disease, with brain metastasis being one of the most common distant metastases of lung adenocarcinoma. This study aimed to investigate the prognostic characteristics of individuals with brain metastases originating from invasive lung adenocarcinoma of distinct pathological subtypes, providing a reference for the management of these patients. Clinical data from 156 patients with lung adenocarcinoma-derived brain metastases were collected, including age, sex, smoking status, Karnofsky Performance Status scores, pathological subtype, lymph node metastasis, tumor site, treatment mode, T stage, and N stage. Patients were classified into two groups (highly differentiated and poorly differentiated) based on their pathological subtypes. Propensity score matching was used to control for confounding factors. The prognostic value of pathological subtypes was assessed using Kaplan-Meier analysis and Cox proportional hazards regression modeling. Kaplan-Meier analysis indicated that patients in the moderately to highly differentiated group had better prognoses. Multivariate analysis revealed that being in the poorly differentiated group was a risk factor for poorer prognosis. Thoracic tumor radiation therapy, chemotherapy, and surgery positively influenced the time interval between lung cancer diagnosis and brain metastasis. The pathological subtypes of lung adenocarcinoma-derived brain metastases are associated with patient prognosis. Patients in the poorly differentiated group have worse prognoses compared to those in the moderately to highly differentiated group. Therefore, patients in the poorly differentiated group may require more frequent follow-ups and aggressive treatment.

Prediction of the pathological subtypes by intraoperative frozen section for patients with cT1N0M0 invasive lung adenocarcinoma (ECTOP-1015): a prospective multicenter study.

This study aims to assess the diagnostic accuracy of the intraoperative frozen section (FS) in determining the pathological subtypes among patients diagnosed with cT1N0M0 invasive lung adenocarcinoma. This was a prospective, multicenter (seven centers in China) clinical trial of Eastern Cooperative Thoracic Oncology Projects (ECTOP-1015). Patients with cT1N0M0 invasive lung adenocarcinoma were enrolled in the study. Pathological images obtained from FS and final pathology (FP) were reviewed by at least two pathologists. The primary endpoint was the concordance between FS and FP diagnoses. The interobserver agreement for identifying pathological subtypes on FS was evaluated among three pathologists. A total of 935 patients were enrolled. The best sensitivity of diagnosing the predominant subtype was 78.2% in the evaluation of the acinar pattern. The presence of an acinar pattern diagnosed by FS was an independent factor for the concordance between FS and FP ( P =0.007, 95% confidence interval: 2.332-4.736). Patients with tumor size >2cm measured by pathology showed a better concordance rate for the predominant subtype (81.6% vs. 74.6%, P =0.023). The presence of radiological ground glass opacity component did not affect the diagnosis accuracy of FS for the predominant subtype (concordance rate: 76.4% vs. 75.2%, P =0.687). Patients with ground glass opacity component showed better accuracy of the identification in the presence of lepidic pattern-predominant adenocarcinoma (82.1% vs. 71.0%, P =0.026). Substantial agreement between the FS diagnosis from three pathologists for the predominant pathological pattern was revealed with κ=0.846. This is the largest prospective trial evaluating FS diagnosing pathological subtype in cT1N0M0 invasive lung adenocarcinoma. A favorable concordance in the assessment of the pathological subtypes between FS and FP was observed, indicating the feasibility of utilizing accurate intraoperative pathological diagnoses from FS in guiding surgical strategies. A combination of radiology could improve the precision of FS.

Live-Cell Invasive Phenotyping Uncovers ALK2 as a Therapeutic Target in LKB1-Mutant Lung Cancer.

The acquisition of invasive properties is a prerequisite for tumor progression and metastasis. Molecular subtypes of KRAS-driven lung cancer exhibit distinct modes of invasion that contribute to unique growth properties and therapeutic susceptibilities. Despite this, pre-clinical strategies designed to exploit growth within the context of invasion are lacking. To address this, we designed an experimental system to screen for targetable signaling pathways linked to active early 3D invasion phenotypes in different molecular subtypes of KRAS-driven lung adenocarcinoma (LUAD). Combined live-cell imaging of human bronchial epithelial cells in a 3D invasion matrix and transcriptomic profiling identified mutant LKB1-specific upregulation of BMP6. LKB1 loss increased BMP6 signaling, which induced the canonical iron regulatory hormone hepcidin. Intact LKB1 was necessary to maintain BMP6 signaling homeostasis and restrict ALK2/BMP6-fueled growth. Pre-clinical studies in a Kras/Lkb1-mutant syngeneic mouse model and in a xenograft model showed potent growth suppression by inhibiting the ALK2/BMP6 signaling axis with single agent inhibitors that are currently in clinical trials. Lastly, BMP6 expression was elevated in LKB1-mutant early-stage lung cancer patient tumors. These results are consistent with a model where LKB1 acts as a 'brake' to iron regulated growth and suggest that ALK2 inhibition can be used for patients with LKB1-mutant tumors.

Roles of naïve CD4+ T cells and their differentiated subtypes in lung adenocarcinoma and underlying potential regulatory pathways

BackgroundNaïve CD4+ T cells and their differentiated counterparts play a significant regulatory role in the tumor immune microenvironment, yet their effects on lung adenocarcinoma (LUAD) are not fully understood.MethodsWe utilized Mendelian randomization to assess the causal association between naïve CD4+ T cells and LUAD. Employing a modified single-sample Gene Set Enrichment Analysis (ssGSEA) algorithm with The Cancer Genome Atlas (TCGA) database, we determined the infiltration levels of naïve CD4+ T cells and their differentiation subtypes and investigated their correlation with clinical characteristics. Potential regulatory pathways of T helper cells were identified through Mantel tests and Kyoto Encyclopedia of Genes and Genomes (KEGG) database enrichment analysis.ResultsMendelian randomization analysis revealed an inhibitory effect of naïve CD4+ T cells on LUAD (false discovery rate < 0.05), which was corroborated by observational experiments using TCGA database. Specifically, T helper cell type 2 demonstrated a promotive effect on LUAD in terms of overall, disease-free, and progression-free survival (p < 0.05). Moreover, regulatory T cells exhibited a protective effect on LUAD in terms of disease-specific survival (p < 0.01). Concurrently, we explored the overall impact of naïve CD4+ T cell differentiation subtypes on LUAD, revealing upregulation in pathways such as neutrophil degranulation, MAPK family signaling pathways, and platelet activation, signaling, and aggregation.ConclusionNaïve CD4+ T cells and their differentiated counterparts play essential regulatory roles in the tumor immune microenvironment, demonstrating bidirectionality in their effects.Thus, elucidating the mechanisms and developing novel cell differentiation-inducing agents will benefit anti-cancer therapy.

Repeatability and reproducibility of deep learning features for lung adenocarcinoma subtypes with nodules less than 10 mm in size: a multicenter thin-slice computed tomography phantom and clinical validation study.

Deep learning features (DLFs) derived from radiomics features (RFs) fused with deep learning have shown potential in enhancing diagnostic capability. However, the limited repeatability and reproducibility of DLFs across multiple centers represents a challenge in the clinically validation of these features. This study thus aimed to evaluate the repeatability and reproducibility of DLFs and their potential efficiency in differentiating subtypes of lung adenocarcinoma less than 10 mm in size and manifesting as ground-glass nodules (GGNs). A chest phantom with nodules was scanned repeatedly using different thin-slice computed tomography (TSCT) scanners with varying acquisition and reconstruction parameters. The robustness of the DLFs was measured using the concordance correlation coefficient (CCC) and intraclass correlation coefficient (ICC). A deep learning approach was used for visualizing the DLFs. To assess the clinical effectiveness and generalizability of the stable and informative DLFs, three hospitals were used to source 275 patients, in whom 405 nodules were pathologically differentially diagnosed as GGN lung adenocarcinoma less than 10 mm in size and were retrospectively reviewed for clinical validation. A total of 64 DLFs were analyzed, which revealed that the variables of slice thickness and slice interval (ICC, 0.79±0.18) and reconstruction kernel (ICC, 0.82±0.07) were significantly associated with the robustness of DLFs. Feature visualization showed that the DLFs were mainly focused around the nodule areas. In the external validation, a subset of 28 robust DLFs identified as stable under all sources of variability achieved the highest area under curve [AUC =0.65, 95% confidence interval (CI): 0.53-0.76] compared to other DLF models and the radiomics model. Although different manufacturers and scanning schemes affect the reproducibility of DLFs, certain DLFs demonstrated excellent stability and effectively improved diagnostic the efficacy for identifying subtypes of lung adenocarcinoma. Therefore, as the first step, screening stable DLFs in multicenter DLFs research may improve diagnostic efficacy and promote the application of these features.

Establishment and verification of novel TNM staging system for lung mucinous adenocarcinoma

BackgroundLung adenocarcinoma is a high-mortality rate cancer. Within this category, Lung mucinous adenocarcinoma (LMAC) is a rare and distinct subtype of lung adenocarcinoma necessitating further investigation. The study was launched to compare the difference of survival features between LMAC and lung non-mucinous adenocarcinoma (LNMAC) and to investigate the significance and demand for developing a new staging system tailored to LMAC.MethodsThis retrospective study assessed the suitableness of the current staging system for LMAC. It compared the overall survival (OS) between LMAC and LNMAC from 2004 to 2020 (LNMAC: 160,387; LMAC: 6,341) and instituted a novel classification framework for LMAC based on US population. Verification group consisting of patients from two Chinese medical centers from 2010 to 2018 (n = 392) was set to ascertain the applicability of this novel system. The primary endpoint was OS. To minimize the bias, propensity score match (PSM) was employed. Survival analysis and Log-rank test were executed to explore the survival features of LMAC.ResultsThe results indicated that the existed staging system was not suitable for LMAC. Patients diagnosed with LMAC exhibited a superior OS compared to those with LNMAC in stage IA2 (P < 0.0001), IA3 (P < 0.0001), IB (P = 0.0062), IIA (P = 0.0090), IIB (P = 0.0005). In contrast, a worse OS in stage IVA (P = 0.0103) was found in LMAC patients. The novel classification system proposed for LMAC proved to be highly applicable and demonstrated substantial efficacy, as confirmed by the verification group.ConclusionThe newly established classification system was more effective for LMAC, but it necessitates large-scale verification to confirm its applicability and reliability.

Subclassification of lung adenocarcinoma through comprehensive multi-omics data to benefit survival outcomes

ObjectivesLung adenocarcinoma (LUAD) is the most common subtype of non-small cell lung cancer. Understanding the molecular mechanisms underlying tumor progression is of great clinical significance. This study aims to identify novel molecular markers associated with LUAD subtypes, with the goal of improving the precision of LUAD subtype classification. Additionally, optimization efforts are directed towards enhancing insights from the perspective of patient survival analysis. Materials and methodsWe propose an innovative feature-selection approach that focuses on LUAD classification, which is comprehensive and robust. The proposed method integrates multi-omics data from The Cancer Genome Atlas (TCGA) and leverages a synergistic combination of max-relevance and min-redundancy, least absolute shrinkage and selection operator, and Boruta algorithms. These selected features were deployed in six machine-learning classifiers: logistic regression, random forest, support vector machine, naive Bayes, k-Nearest Neighbor, and XGBoost. ResultsThe proposed approach achieved an area under the receiver operating characteristic curve (AUC) of 0.9958 for LR. Notably, the accuracy and AUC of a composite model incorporating copy number, methylation, as well as RNA- sequencing data for expression of exons, genes, and miRNA mature strands surpassed the accuracy and AUC metrics of models with single-omics data or other multi-omics combinations. Survival analyses, revealed the SVM classifier to elicit optimal classification, outperforming that achieved by TCGA. To enhance model interpretability, SHapley Additive exPlanations (SHAP) values were utilized to elucidate the impact of each feature on the predictions. Gene Ontology (GO) enrichment analysis identified significant biological processes, molecular functions, and cellular components associated with LUAD subtypes. ConclusionIn summary, our feature selection process, based on TCGA multi-omics data and combined with multiple machine learning classifiers, proficiently identifies molecular subtypes of lung adenocarcinoma and their corresponding significant genes. Our method could enhance the early detection and diagnosis of LUAD, expedite the development of targeted therapies and, ultimately, lengthen patient survival.

Primary pulmonary enteric adenocarcinoma: A case report and review of literature

Primary pulmonary enteric adenocarcinoma (P-PEAC) is an unusual subtype of invasive lung adenocarcinoma. The significance of such tumor lies in its similar histopathological and immunohistochemical characteristics to colorectal adenocarcinoma. Therefore, a detailed investigation is crucial to establish a definitive diagnosis. This paper reports a case of a 51-year-old Saudi female, diagnosed with a rare variant of lung carcinoma, P-PEAC.

Integrated multi-omics analysis and machine learning to refine molecular subtypes, prognosis, and immunotherapy in lung adenocarcinoma.

Lung adenocarcinoma (LUAD) has a malignant characteristic that is highly aggressive and prone to metastasis. There is still a lack of suitable biomarkers to facilitate the refinement of precision-based therapeutic regimens. We used a combinationof10 known clustering algorithms and the omics data from 4 dimensions to identify high-resolution molecular subtypes of LUAD. Subsequently, consensus machine learning-related prognostic signature (CMRS) was developed based on subtypes related genes and an integrated program framework containing 10 machine learning algorithms. The efficiency of CMRS was analyzed from the perspectives of tumor microenvironment, genomic landscape, immunotherapy, drug sensitivity, and single-cell analysis. In terms of results,through multi-omics clustering, we identified 2 comprehensive omics subtypes (CSs) in which CS1 patients had worse survival outcomes, higher aggressiveness, mRNAsi and mutation frequency. Subsequently, we developed CMRS based on 13 key genes up-regulated in CS1. The prognostic predictive efficiency of CMRS was superior to most established LUAD prognostic signatures. CMRS demonstrated a strong correlation with tumor microenvironmental feature variants and genomic instability generation. Regarding clinical performance, patients in the high CMRS group were more likely to benefit from immunotherapy, whereas low CMRS were more likely to benefit from chemotherapy and targeted drug therapy. In addition, we evaluated that drugs such as neratinib, oligomycin A, and others may be candidates for patients in the high CMRS group. Single-cell analysis revealed that CMRS-related genes were mainly expressed in epithelial cells.The novel molecular subtypes identified in this study based on multi-omics data could provide new insights into the stratified treatment of LUAD, while the development of CMRS could serve as a candidate indicator of the degree of benefit of precision therapy and immunotherapy for LUAD.

Development and validation of a clinical decision tool for preoperative micropapillary and solid pattern lung adenocarcinoma of CT≤2cm.

Micropapillary (MP) and solid(S) pattern adenocarcinoma are highly malignant subtypes of lung adenocarcinoma. In today's era of increasingly conservative surgery for small lung cancer, effective preoperative identification of these subtypes is greatly important for surgical planning and long term survival of patients. For this retrospective study, the presence of MP and/or S was evaluated in 2167 consecutive patients who underwent surgical resection for clinical stage IA1-2 lung adenocarcinoma. MP and/or S pattern-positive patients and negative-pattern patients were matched at a ratio of 1:3. The Lasso regression model was used for data dimension reduction and imaging signature building. Multivariate logistic regression was used to establish the predictive model, presented as an imaging nomogram. The performance of the nomogram was assessed based on calibration, identification, and clinical usefulness, and internal and external validation of the model was conducted. The proportion of solid components (PSC), Sphericity, entropy, Shape, bronchial honeycomb, nodule shape, sex, and smoking were independent factors in the prediction model of MP and/or S lung adenocarcinoma. The model showed good discrimination with an area under the ROC curve of 0.85. DCA demonstrated that the model could achieve good benefits for patients. RCS analysis suggested a significant increase in the proportion of MP and/or S from 11% to 48% when the PSC value was 68%. Small MP and/or S adenocarcinoma can be effectively identified preoperatively by their typical 3D and 2D imaging features.

Identification of lung adenocarcinoma subtypes based on mitochondrial energy metabolism-related genes

Background Identifying subtypes of lung adenocarcinoma (LUAD) patients based on mitochondrial energy metabolism and immunotherapy sensitivity is essential for precision cancer treatment. Methods LUAD subtypes were identified using unsupervised consensus clustering, and results were subjected to immune and tumor mutation analyses. DEGs between subtypes were identified by differential analysis. Functional enrichment and PPI network analyses were conducted. Patients were classified into high and low expression groups based on the expression of the top 10 hub genes, and survival analysis was performed. Drugs sensitive to feature genes were screened based on the correlation between hub gene expression and drug IC50 value. qRT-PCR and western blot were used for gene expression detection, and CCK-8 and flow cytometry were for cell viability and apoptosis analysis. Results Cluster-1 had significantly higher overall survival and a higher degree of immunoinfiltration and immunophenotypic score, but a lower TIDE score, DEPTH score, and TMB. Enrichment analysis showed that pathways and functions of DEGs between two clusters were mainly related to the interaction of receptor ligands with intracellular proteases. High expression of hub genes corresponded to lower patient survival rates. The predicted drugs with high sensitivity to feature genes were CDK1: Ribavirin (0.476), CCNB2: Hydroxyurea (0.474), Chelerythrine (0.470), and KIF11: Ribavirin (0.471). KIF11 and CCNB2 were highly expressed in LUAD cells and promoted cell viability and inhibited cell apoptosis. Conclusion This study identified two subtypes of LUAD, with cluster-1 being more suitable for immunotherapy. These results provided a reference for the development of precision immunotherapy for LUAD patients.

Res-TransNet: A Hybrid deep Learning Network for Predicting Pathological Subtypes of lung Adenocarcinoma in CT Images

Res-TransNet: A Hybrid deep Learning Network for Predicting Pathological Subtypes of lung Adenocarcinoma in CT Images